Zhou, J. orcid.org/0009-0009-6317-6373, Huang, L. orcid.org/0009-0002-1093-8302, Xia, H. orcid.org/0009-0007-8115-1693 et al. (6 more authors) (2024) LVF2: A statistical timing model based on Gaussian mixture for yield estimation and speed binning. In: DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference. DAC '24: 61st ACM/IEEE Design Automation Conference, 23-27 Jun 2024, San Francisco, CA, USA. ACM ISBN 9798400706011
Abstract
As transistor size continues to scale down, process variation has become an essential factor determining semiconductor yield and economic return. The Liberty Variation Format (LVF) is the current industrial standard that expresses statistical timing behaviors based on single Gaussian model. However, it loses accuracy when the timing distribution is non-Gaussian due to growing process variations. This paper proposes a novel LVF2 distribution model that combines two weighted skewed-normal (SN) distributions, which better captures the multi-Gaussian timing distribution while maintaining backward compatibility with LVF. Experiments using TSMC 22nm standard cells show that, compared to LVF, LVF2 reduces binning error by 7.74X in delay and 9.56X in transition time, and reduces 3σ-yield error by 4.79X and 7.18X in delay and transition time, respectively. The error reduction for path delay is diminished due to Central Limit Theorem (CLT). But it is still 2X for a typical circuit path with 8 Fanout-of-4 (FO4) inverter delays.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | speed binning; yield estimation; statistical timing modeling; process variation; LVF |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Jan 2025 10:41 |
Last Modified: | 17 Jan 2025 10:41 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3649329.3655670 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:221555 |